Analyze systems of equations, finding the best approximation to a solution; vector space of matrices and polynomials; coordinate vectors, change of coordinate system; inner product space; linear transformations between general vector spaces; approximating a trigonometric function by a polynomial.

A calculus-based introduction to probability theory and its applications in engineering and applied science. Includes counting techniques, conditional probability, independence, discrete and continuous random variables, probability distribution functions, expected value and variance, joint distributions, covariance, correlation, the Central Limit theorem, the Poisson process, an introduction to statistical inference. Students must have completed (APMA 2120 or MATH 2310 or MATH 2315) AND (CS 1110 or CS 1111 or CS 1112 or CS 1113 or successfully completed the CS 1110 place out test).

Introduces basic concepts of probability such as random variables, single and joint probability distributions, and the central limit theorem. The course then emphasizes applied statistics, including descriptive statistics, statistical inference, confidence intervals, hypothesis testing, correlation, linear regression, and ANOVA. Students cannot receive credit for both this course and APMA 3120. Prerequisite: APMA 2120 or equivalent.

Includes point estimation methods, confidence intervals, hypothesis testing for one population and two populations, categorical data tests, single and multi-factor analysis of variance (ANOVA) techniques, linear and non-linear regression and correlation analysis, and non-parametric tests. Students cannot receive credit for both this course and APMA 3110. Prerequisite: APMA 3100 or MATH 3100.

Partial differential equations that govern physical phenomena in science and engineering. Separation of variables, superposition, Fourier series, Sturm-Liouville eigenvalue problems, eigenfunction expansion techniques. Particular focus on the heat, wave, and Laplace partial differential equations in rectangular, cylindrical, and spherical coordinates. Prerequisites: (APMA 2120 or MATH 2310 or MATH 2315) AND (APMA 2130 or MATH 3250 or APMA 2501 topic Diff Equations & Linear Algebra)

This course uses a Case-Study approach to teach statistical techniques with R: confidence intervals, hypotheses tests, regression, and anova. Also, it covers major statistical learning techniques for both supervised and unsupervised learning. Supervised learning topics cover regression and classification, and unsupervised learning topics cover clustering & principal component analysis. Prior basic statistic skills are needed.

Topics include analytic functions, Cauchy Theorems and formulas, power series, Taylor and Laurent series, complex integration, residue theorem, conformal mapping, and Laplace transforms. Prerequisite: APMA 2120 or MATH 2310 or APMA 2512 - Honors Engineering Mathematics II.

Applies mathematical techniques to special problems of current interest. Topic for each semester are announced at the time of course enrollment.

Reading and research under the direction of a faculty member. Prerequisite: Fourth-year standing.

Introduces techniques used in obtaining numerical solutions, emphasizing error estimation. Includes approximation and integration of functions, and solution of algebraic and differential equations. Prerequisite: Two years of college mathematics, including some linear algebra and differential equations, and the ability to write computer programs in any language.